ARTICLE OPEN ACCESS Genetic and lifestyle risk factors for MRI-defined brain infarcts in a population-based setting

Ganesh Chauhan, PhD,* Hieab H.H. Adams, PhD,* Claudia L. Satizabal, PhD,* Joshua C. Bis, PhD,* Correspondence Alexander Teumer, PhD,* Muralidharan Sargurupremraj, PhD,* Edith Hofer, PhD, Stella Trompet, PhD, Dr. Debette Saima Hilal, PhD, Albert Vernon Smith, PhD, Xueqiu Jian, PhD, Rainer Malik, PhD, Matthew Traylor, PhD, stephanie.debette@ Sara L. Pulit, PhD, Philippe Amouyel, MD, Bernard Mazoyer, MD, PhD, Yi-Cheng Zhu, MD, Sara Kaffashian, PhD, u-bordeaux.fr Sabrina Schilling, PhD, Gary W. Beecham, PhD, Thomas J. Montine, MD, Gerard D. Schellenberg, PhD, or Dr. Longstreth Olafur Kjartansson, MD, Vilmundur Guðnason, MD, PhD, David S. Knopman, MD, Michael E. Griswold, PhD, [email protected] B. Gwen Windham, MD, Rebecca F. Gottesman, MD, Thomas H. Mosley, PhD, Reinhold Schmidt, MD, Yasaman Saba, MSc, Helena Schmidt, MD, Fumihiko Takeuchi, PhD, Shuhei Yamaguchi, MD, Toru Nabika, MD, Norihiro Kato, MD, Kumar B. Rajan, PhD, Neelum T. Aggarwal, MD, Philip L. De Jager, MD, Denis A. Evans, MD, Bruce M. Psaty, MD, Jerome I. Rotter, MD, Kenneth Rice, PhD, Oscar L. Lopez, MD, Jiemin Liao, MA, Christopher Chen, FRCP, Ching-Yu Cheng, MD, Tien Y. Wong, MD, Mohammad K. Ikram, MD, Sven J. van der Lee, MD, Najaf Amin, PhD, Vincent Chouraki, MD, Anita L. DeStefano, PhD, Hugo J. Aparicio, MD, Jose R. Romero, MD, Pauline Maillard, PhD, Charles DeCarli, MD, Joanna M. Wardlaw, MD, Maria del C. Vald´es Hern´andez, PhD, Michelle Luciano, PhD, David Liewald, BSc, Ian J. Deary, PhD, John M. Starr, PhD, Mark E. Bastin, PhD, Susana Muñoz Maniega, PhD, P. Eline Slagboom, PhD, Marian Beekman, PhD, Joris Deelen, PhD, Hae-Won Uh, PhD, Robin Lemmens, MD, Henry Brodaty, MD, Margaret J. Wright, PhD, David Ames, MD, Giorgio B. Boncoraglio, MD, Jemma C. Hopewell, PhD, Ashley H. Beecham, MS, Susan H. Blanton, PhD, Clinton B. Wright, MD, Ralph L. Sacco, MD, Wei Wen, PhD, Anbupalam Thalamuthu, PhD, Nicola J. Armstrong, PhD, Elizabeth Chong, PhD, Peter R. Schofield, PhD, John B. Kwok, PhD, Jeroen van der Grond, PhD, David J. Stott, MBChB, MD, Ian Ford, PhD, J. Wouter Jukema, MD, Meike W. Vernooij, MD, Albert Hofman, MD, Andr´e G. Uitterlinden, PhD, Aad van der Lugt, MD, Katharina Wittfeld, PhD, Hans J. Grabe, MD, Norbert Hosten, MD, Bettina von Sarnowski, MD, Uwe Volker,¨ PhD, Christopher Levi, BMedSci, FRACP, Jordi Jimenez-Conde, MD, Pankaj Sharma, MD, PhD, Cathie L.M. Sudlow, FRCP(Ed), Jonathan Rosand, MD, Daniel Woo, MD, John W. Cole, MD, James F. Meschia, MD, Agnieszka Slowik, MD, Vincent Thijs, MD, Arne Lindgren, MD, Olle Melander, MD, Raji P. Grewal, MD, Tatjana Rundek, MD, Kathy Rexrode, MD, Peter M. Rothwell, MD, Donna K. Arnett, PhD, Christina Jern, MD, Julie A. Johnson, PharmD, Oscar R. Benavente, MD, Sylvia Wasssertheil-Smoller, PhD, Jin-Moo Lee, MD, PhD, Quenna Wong, MS, Braxton D. Mitchell, PhD, Stephen S. Rich, PhD, Patrick F. McArdle, PhD, Mirjam I. Geerlings, PhD, Yolanda van der Graaf, MD, PhD, Paul I.W. de Bakker, PhD, Folkert W. Asselbergs, MD, Velandai Srikanth, FRACP, Russell Thomson, PhD, Rebekah McWhirter, PhD, Chris Moran, FRACP, Michele Callisaya, PhD, Thanh Phan, FRACP, Loes C.A. Rutten-Jacobs, PhD, Steve Bevan, PhD, Christophe Tzourio, MD, PhD, Karen A. Mather, PhD, Perminder S. Sachdev, MD, Cornelia M. van Duijn, PhD, Bradford B. Worrall, MD, Martin Dichgans, MD, Steven J. Kittner, MD, Hugh S. Markus, FMedSci, Mohammad A. Ikram, MD,‡ Myriam Fornage, PhD,‡ Lenore J. Launer, PhD,‡ Sudha Seshadri, MD,‡ W.T. Longstreth, Jr., MD,‡ and St´ephanie Debette, MD‡,on behalf of the Stroke Genetics Network (SiGN), the International Stroke Genetics Consortium (ISGC), METASTROKE, Alzheimer’s Disease Genetics Consortium (ADGC), and the Neurology Working Group of the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) Consortium Neurology® 2019;92:e486-e503. doi:10.1212/WNL.0000000000006851

*These authors contributed equally to this work.

‡These authors jointly supervised this work.

Author affiliations appear on pages e496–e498.

Coinvestigators are listed at links.lww.com/WNL/A800.

Go to Neurology.org/N for full disclosures. Funding information and disclosures deemed relevant by the authors, if any, are provided at the end of the article.

The Article Processing Charge was funded by The University of Edinburgh. This is an open access article distributed under the terms of the Creative Commons Attribution License 4.0 (CC BY), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. e486 Copyright © 2019 The Author(s). Published by Wolters Kluwer Health, Inc. on behalf of the American Academy of Neurology. Abstract Objective To explore genetic and lifestyle risk factors of MRI-defined brain infarcts (BI) in large population-based cohorts.

Methods We performed meta-analyses of genome-wide association studies (GWAS) and examined associations of vascular risk factors and their genetic risk scores (GRS) with MRI-defined BI and a subset of BI, namely, small subcortical BI (SSBI), in 18 population-based cohorts (n = 20,949) from 5 ethnicities (3,726 with BI, 2,021 with SSBI). Top loci were followed up in 7 population-based cohorts (n = 6,862; 1,483 with BI, 630 with SBBI), and we tested associations with related phenotypes including ischemic stroke and pathologically defined BI.

Results The mean prevalence was 17.7% for BI and 10.5% for SSBI, steeply rising after age 65. Two loci − showed genome-wide significant association with BI: FBN2, p =1.77×10 8;andLINC00539/ − ZDHHC20, p =5.82×109. Both have been associated with blood pressure (BP)–related phenotypes, but did not replicate in the smaller follow-up sample or show associations with related phenotypes. Age- and sex-adjusted associations with BI and SSBI were observed for BP traits −25 −14 (p value for BI, p[BI] =9.38×10 ; p[SSBI] =5.23×10 for hypertension), smoking (p[BI] =4.4× −10 −4 −8 −3 10 ; p[SSBI] =1.2×10 ), diabetes (p[BI] =1.7×10 ; p[SSBI] =2.8×10 ), previous cardio- −18 −7 −69 vascular disease (p[BI] =1.0×10 ; p[SSBI] =2.3×10 ), stroke (p[BI] =3.9×10 ; p[SSBI] =3.2× −24 fi −157 10 ), and MRI-de ned white matter hyperintensity burden (p[BI] = 1.43 × 10 ; p[SSBI] = − 3.16 × 10 106), but not with body mass index or cholesterol. GRS of BP traits were associated with BI and SSBI (p ≤ 0.0022), without indication of directional pleiotropy.

Conclusion In this multiethnic GWAS meta-analysis, including over 20,000 population-based participants, we identified genetic risk loci for BI requiring validation once additional large datasets become available. High BP, including genetically determined, was the most significant modifiable, causal risk factor for BI.

Introduction association studies (GWAS) from 18 population-based studies, comprising 20,949 participants from 5 ethnic Brain infarcts (BI) detected on MRI are commonly seen in groups, using the 1000 Genomes reference panel (1000G), older persons, being described in 8%–28% of participants in more than doubling the size of a prior GWAS.16 Second, we 1 fi population-based cohort studies. Most MRI-de ned BI are examined the association of vascular risk factors with BI and covert, not being associated with overt, clinical stroke SSBI in this large sample, using both vascular risk factor 2,3 symptoms. Nonetheless, they cannot be considered silent measurements and their genetic risk scores (GRS). or benign, as they are often associated with subtle neurologic symptoms and with increased risk of future stroke, cognitive decline, and in some studies dementia.4,5 Most MRI-defined Methods BI are small subcortical BI (SSBI), believed to be primarily caused by small vessel disease (SVD).6 Study design and samples The meta-analyses included 18 prospective population-based Mechanisms and predictors of BI and SSBI remain incompletely cohorts participating in the Cohorts for Heart and Aging understood. No genetic risk variantsforBIandSSBIhavebeen Research in Genomic Epidemiology (CHARGE) consortium – consistently identified to date,7 16 and findings with vascular (table e-1 and additional Methods e-1, doi.org/10.5061/ risk factors have been inconsistent.1 Partly reflecting this un- dryad.hk07677). Although the cohorts contributing partic- certainty, recommendations to direct clinicians on how to best ipants are longitudinal, this study is cross-sectional, based on manage covert MRI-defined BI are lacking. the analysis of BI and SSBI at one timepoint in the subset of cohort participants with brain MRI. These cohorts comprised To enhance understanding of risk factors for BI and SSBI, we 5 ethnic groups and ancestries: European (n = 17,956), Af- first conducted a large meta-analysis of genome-wide rican (n = 1,834), Hispanic (n = 737), Malay (n = 215), and

Neurology.org/N Neurology | Volume 92, Number 5 | January 29, 2019 e487 Glossary 1000G = 1000 Genomes reference panel; BI = brain infarcts; BMI = body mass index; BP = blood pressure; DBP = diastolic blood pressure; FLAIR = fluid-attenuated inversion recovery; GRS = genetic risk scores; GWAS = genome-wide association studies; HDL = high-density lipoprotein; IS = ischemic stroke; IS-SVD = small vessel disease subtype of ischemic stroke; IVW = inverse-variance weighting; L10BF = Log10 of Bayesian factor; LD = linkage disequilibrium; LDL = low-density lipoprotein; MAP = mean arterial pressure; PP = pulse pressure; SNP = single nucleotide polymorphism; SBP = systolic blood pressure; SSBI = small subcortical brain infarcts; SVD = small vessel disease; WMH = white matter hyperintensities.

Chinese (n = 207). Some cohorts contributed to data for cholesterol, high-density lipoprotein (HDL) cholesterol, and more than one ethnic group, resulting in a total of 23 datasets triglycerides were measured using enzymatic methods. Low- (tables e-1 to e-3, doi.org/10.5061/dryad.hk07677). Out of density lipoprotein (LDL) cholesterol was calculated using a total of 20,949 participants, 3,726 had MRI-defined BI. We the Friedewald formula. Body mass index (BMI) was defined did not exclude participants with a history of overt, clinically as the ratio of weight (kg) to the square of height (m). Active defined stroke prior to the MRI, except in 4 cohorts where smoking was defined according to study-specific criteria. patients with history of stroke were excluded by design. Three History of overt, clinically defined stroke and other cardio- datasets did not contribute to the SSBI analysis either due to vascular events was based on ongoing surveillance prior to small numbers or absence of BI subtyping. Out of a total of brain MRI acquisition in most studies since participant re- 19,073 participants in the remaining 20 datasets, 3,533 had BI, cruitment had started prior to the initial brain MRI. In studies of whom 2,021 (57.2%) had SSBI. that had brain MRI scanning at the initial visit, the history and examination at this visit were used to identify prior overt, Variable definitions clinically defined stroke. History of cardiovascular events in- Detailed MRI scanning protocols, as well as BI and SSBI cluded history of angina, myocardial infarction, cardiac bypass definitions, for each study are described in table e-4 (doi.org/ surgery, angioplasty, or peripheral vascular disease. 10.5061/dryad.hk07677). All protocols comprised at least T1, T2, and proton density or fluid-attenuated inversion recovery Genotypes (FLAIR) sequences. On MRI, BI were defined as an area of All participating discovery cohorts had genome-wide geno- abnormal signal intensity lacking mass effect with a size types imputed on the 1000G (phase 1, version 3).19 Genome- ≥3–4 mm; in the white matter, they were required to be wide genotyping platforms, quality control measures, and hypointense on T1-weighted images, approaching the hypo- imputation parameters used in each study are presented in intensity of CSF, to distinguish them from diffuse white tables e-5–e-7 (doi.org/10.5061/dryad.hk07677). matter lesions; and they were distinguished from dilated perivascular spaces based on their irregular shape, presence of Genome-wide association analyses with BI and a hyperintense rim in FLAIR, and absence of a typical vascular small subcortical BI shape following the orientation of perforating vessels.17 SSBI For genome-wide association analyses with BI and SSBI, each corresponded to BI with a size <15–20 mm, located in the study performed logistic regression under an additive genetic basal ganglia, the white matter, or the brainstem. Participants model after adjusting for age, sex, principal components of with large BI or BI located in the cerebral cortex or cerebellum population stratification, and additional study-specific cova- were excluded from analyses of SSBI. We also measured riates, such as study site or family structure, as needed (ad- burden of white matter hyperintensities (WMH), a quantita- ditional Methods e-2, doi.org/10.5061/dryad.hk07677, for tive MRI marker of SVD, corresponding to signal abnormal- centralized quality control description). Our primary multi- ities of variable size in the white matter, appearing as ethnic GWAS meta-analysis was performed using MANTRA, 20 hyperintensity on T2-weighted or FLAIR images, but without based on a Bayesian framework. In secondary analyses, we cavitation. Details of WMH measurements have been de- also ran the multiethnic GWAS meta-analysis with 2 alter- scribed previously.18 native methods (additional Methods e-2, doi.org/10.5061/ dryad.hk07677): (1) using fixed effects inverse variance Vascular risk factors weighting with METAL21,22 and (2) using the random effects Vascular risk factor levels measured closest to brain MRI ac- meta-analysis model implemented in METASOFT.23 During quisition were used. Hypertension was defined as systolic meta-analysis, genomic control correction was applied to the blood pressure (SBP) ≥140 mm Hg or diastolic blood pres- individual studies and ethnic-specific results to remove any sure (DBP) ≥90 mm Hg or use of one or more blood pressure residual inflation of association statistics. We did not observe (BP)–lowering medications. We defined pulse pressure (PP) any systematic inflation of association statistics (figure e-1, as the difference between SBP and DBP and mean arterial doi.org/10.5061/dryad.hk07677). Statistical measures from pressure (MAP) as DBP + 1/3 × PP. Diabetes was defined as MANTRA, the primary meta-analysis method, were used to fi fi a previous diagnosis of diabetes, a fasting plasma glucose >7.0 de ne genome-wide signi cance (Log10 of Bayesian factor 24 mmol/L, or antidiabetic drug use. Fasting serum total [L10BF] > 6) and to choose single nucleotide e488 Neurology | Volume 92, Number 5 | January 29, 2019 Neurology.org/N polymorphisms (SNPs) for follow-up (L10BF > 4.5) in either and subsequently the betas and standard errors obtained in the BI or SSBI meta-analysis. Details of functional annotation each ethnic group were combined using fixed-effects in- of top loci are provided in additional Methods e-3 (doi.org/ verse variance-weighted meta-analysis, in the absence of − 10.5061/dryad.hk07677). heterogeneity (p <1×106), to derive the multiethnic meta-analysis estimates. For WMH burden, the statistics Follow-up and extension were combined using the Z score–based sample size For follow-up and extension studies, genotypes imputed to weightedmeta-analysisasWMHburdenwasmeasuredon the 1000G reference panel were available in most instances different scales in participating studies.18 for in silico look-up of the selected risk variants. Three follow-up studies performed de novo genotyping of the top We then explored whether genetic variants previously 6 loci (additional Methods e-1, doi.org/10.5061/dryad. shown in published GWAS to be associated with specific hk07677). The lead variant (with lowest p value) was gen- vascular risk factors were, in aggregate, also associated with otyped at each suggestive or genome-wide significant locus, BI and SSBI. This approach was selected to assess to what and if not feasible, another variant in strong linkage dis- extent genetically determined vascular risk factor levels are 2 equilibrium (LD, r > 0.8) was genotyped. A p value associated with BI and SSBI and to provide evidence for <0.0083, correcting for 6 loci, was considered significant a causal relation between a given vascular risk factor and risk evidence for replication. of BI or SSBI, provided that Mendelian randomization assumptions are fulfilled.26 We combined known genetic The follow-up sample, in which we sought to confirm asso- risk variants for each individual vascular risk factor into ciations observed in the discovery analysis, included 6,862 a weighted GRS, using effect estimates from the largest participants, of whom 1,483 had BI and 630 had SSBI, from 6 published GWAS of that risk factor as weights. We then community-based studies of European origin and one of tested for association of these GRS with BI and SSBI using Japanese origin (table e-1, doi.org/10.5061/dryad.hk07677). the inverse-variance weighting (IVW) method. Construc- tion of the GRS, selection of variants for the GRS analysis, as As an extension, to test whether genetic variants associated well as effect estimates used as weights are detailed in ad- with MRI-defined BI or SSBI in the discovery analysis are also ditional Methods e-4 and tables e-9–e-12 (doi.org/10. associated with correlated phenotypes, we first explored their 5061/dryad.hk07677). For significant GRS associations association with ischemic stroke (IS) overall and the small with BI or SSBI, we further conducted sensitivity analyses vessel disease subtype (IS-SVD) when available in 4 collab- using the MR-Egger method implemented as an R package orative studies (table e-1, doi.org/10.5061/dryad.hk07677). (TwoSampleMR),27 which unlike the IVW method esti- Second, we explored whether genetic variants associated with mates the intercept term as part of the analysis. An intercept MRI-defined BI and SSBI were associated with neuro- term significantly differing from zero suggests the presence pathologically defined BI based on 2,940 brain autopsies in of directional (unbalanced) pleiotropy, meaning that the participants without dementia from the Alzheimer’s Disease pleiotropic effects of genetic variants are not balanced about Genetics Consortium (ADGC). Participants with large the null.27 We used a conservative significance threshold of infarcts or lacunes (n = 857, 29%) were compared to partic- p < 0.05 for the intercept. ipants without any infarcts or having only microscopic infarcts 25 (n = 2,083). After Bonferroni correction for 12 independent vascular phenotypes tested for association with BI and SSBI, p < We calculated power of the follow-up and extension studies 0.0042 was considered significant for associations with vas- using Quanto V1.2.3 (biostats.usc.edu/software; table e-8 and cular risk factor measurements or GRS. The number of in- figure e-2, doi.org/10.5061/dryad.hk07677). dependent vascular phenotypes, taking into account correlation between the phenotypes considered, was esti- Association of vascular risk factors with BI mated based on individual level data from the 3C-Dijon study and SSBI using the online tool matSpDlite (neurogenetics.qimr- Individual studies performed logistic regression to test for berghofer.edu.au/matSpDlite/). association of vascular risk factor measurements with pres- ence or absence of at least one BI or SSBI. Analyses were Standard protocol approvals, registrations, performed with and without adjustments for age and sex. and patient consents Analyses with BP or lipid traits as the main independent Institutional review boards approved all of these studies, and variable were additionally adjusted for treatment with disease- all participants provided informed consent. specific medications, and association analyses with fasting plasma glucose were limited to participants without type Data availability 2 diabetes. Except for WMH burden, the regression coef- Summary statistics of the top SNPs are available from Dryad ficients and standard errors for risk factors in the individual for both BI and SSBI. Other data that support the findings of studies belonging to one ethnic group were combined us- this study are available from the corresponding authors upon ing fixed-effects inverse variance-weighted meta-analysis reasonable request.

Neurology.org/N Neurology | Volume 92, Number 5 | January 29, 2019 e489 Figure 1 Prevalence of MRI-defined brain infarcts (BI) and small subcortical brain infarcts (SSBI) by different age groups

CI = confidence interval.

fi Results a suggestive level of signi cance (L10BF > 4.5): rs12373108 near CALB2/ZNF23 (chr16q22) and rs74587705 in SV2B In this large population-based dataset comprising 18 cohort (chr15q26) (table 1). No genome-wide significant association fi studies, the frequency of MRI-de ned BI ranged from 4% to was observed for SSBI, but 2 loci reached the threshold for 38% in participating cohorts (table e-1, doi.org/10.5061/ suggestive association (L10BF > 4.5): rs9371194 in PLEKHG1 dryad.hk07677). A description of demographic characteristics (chr6q25) and rs75889566 in FRMD1 (chr6q27, table 1). in all participants with BI (n = 3,726), with SSBI (n = 2,021), These 6 loci were taken forward for the follow-up stage and without BI (n = 17,223) is provided in tables e-2 and e-3 (table 2). For all SNPs reaching Log BF > 4.5 in the discovery (doi.org/10.5061/dryad.hk07677) for individual studies. 10 stage, association statistics are shown in table e-13 and figure e-7 Participants with BI and SSBI were on average 6 years older (doi.org/10.5061/dryad.hk07677). and more often men compared to those without BI. In age- stratified analyses, the prevalence of BI and SSBI increased In the substantially smaller population-based follow-up with age, most prominently beyond age 65, after which studies, we could not replicate the 2 genome-wide signifi- a 25.8% (range 13.9%–37.0%) increment in BI prevalence was cant or the 4 suggestive loci associated with BI or SSBI (table observed compared to participants younger than 65 years 2). Of the 6 loci that we followed up, we had limited power for (figure 1). Overall, the prevalence of BI ranged from less than 5% before age 50 to over 30% beyond age 80, with similar 2 of the loci for BI (52%) and 4 of the loci for SSBI – findings when we analyzed men and women separately (fig- (50% 58%) (table e-8, doi.org/10.5061/dryad.hk07677). ures e-3 and e-4, doi.org/10.5061/dryad.hk07677). Only 11% Power estimates in the follow-up study are even lower when ’ of those with BI and 9% of those with SSBI had a history of accounting for the winner s curse phenomenon, which leads fl ff 28 stroke (12.5% and 9.8% when removing cohorts that excluded to in ated e ect estimates in the discovery cohort. One participants with history of stroke by design); hence, the vast suggestive locus for SSBI (PLEKHG1) showed nominal as- majority of MRI-defined BI were covert. sociation with BI and SSBI in the follow-up studies (pBI = 0.03 and pSSBI = 0.02), but in the opposite direction (table 2). Genome-wide association plots for GWAS of BI and SSBI are displayed in figures e-5 and e-6 (doi.org/10.5061/dryad. Likewise, none of the genome-wide significant or suggestive hk07677). Two loci were associated with risk of BI at genome- loci for BI and SSBI showed association with IS (overall or IS- fi fi wide signi cant level (L10BF > 6): rs39938 in FBN2 (chr5q23) SVD) or pathologically de ned BI in the extension studies and rs12583648 in LINC00539 and near ZDHHC20 after correcting for multiple testing (table 2 and table e-14, (chr13q12). In addition, 2 SNPs were associated with BI at doi.org/10.5061/dryad.hk07677). Whereas the sample size e490 Neurology | Volume 92, Number 5 | January 29, 2019 Neurology.org/N Neurology.org/N

Table 1 Loci reaching Log10 of Bayesian factor (L10 BF) >4.5 in the discovery stage of the genome-wide association studies with brain infarcts (BI) or small subcortical brain infarcts (SSBI)

Bayesian- Multiethnic based Multiethnic fixed-effects random-effects approach of Function meta-analysis meta-analysis MANTRA Lead SNP Chr (distance Minor allele Cases/ Genes (chr:position) region from gene) frequency Phenotype OR (95% CI) pFE p-het pRE L10 BF p-het controls, n

− − FBN2 rs39938 (5:127663579) 5q23 Intronic T (0.21) BI 1.21 (1.13–1.30) 1.77 × 10 8a 0.42 4.83 × 10 8 6.52a 0.31 3,603/16,464

− − SSBI 1.23 (1.13–1.34) 3.93 × 10 6 0.58 1.43 × 10 5 4.28 0.25 1975/13,260

− − PLEKHG1 rs9371194 (6:151034730) 6q25 Intronic T (0.46) BI 1.12 (1.06–1.18) 5.94 × 10 5 0.47 4.28 × 10 4 3.10 0.16 3,726/17,223

− − SSBI 1.19 (1.11–1.28) 1.90 × 10 6 0.50 3.63 × 10 5 4.54 0.18 2,112/15,432

− − FRMD1 rs75889566 (6:168476856) 6q27 Intronic T (0.08) BI 0.82 (0.73–0.92) 7.71 × 10 4 0.47 2.03 × 10 3 1.99 0.27 3,181/12,731

− − SSBI 0.65 (0.55–0.78) 1.82 × 10 6 1.00 4.40 × 10 5 4.63 0.17 1,584/8,538

− − LINC00539/ZDHHC20 rs12583648 (13:21900055) 13q12 Intronic C (0.33) BI 1.21 (1.13–1.29) 5.82 × 10 9a 0.29 2.33 × 10 7 7.00a 0.22 3,685/17,085

− − SSBI 1.20 (1.10–1.30) 2.95 × 10 5 0.26 7.66 × 10 5 3.07 0.30 1985/14,705 erlg oue9,Nme aur 9 2019 29, January | 5 Number 92, Volume | Neurology − − SV2B rs74587705 (15:91764992) 15q26 Intronic T (0.03) BI 1.85 (1.46–2.34) 3.36 × 10 7 0.62 5.69 × 10 6 5.25 0.29 2,291/6,072

SSBIb ———————

− − CALB2/ZNF23 rs12373108 (16:71432507) 16q22 Intergenic T (0.17) BI 1.21 (1.12–1.31) 5.02 × 10 7 0.66 1.53 × 10 5 4.90 0.21 3,418/15,607 (8.1 kb)

− − SSBI 1.23 (1.12–1.36) 1.40 × 10 5 0.47 1.37 × 10 4 3.61 0.18 1,851/12,794

Abbreviations: chr = chromosome (chromosomal positions are based on GRCh37); CI = confidence interval; FE = fixed effect; OR = odds ratio; p-het = p for heterogeneity; RE = random effect; SNP = single nucleotide polymorphism. a Loci reaching genome-wide significance, L10 BF >6. b Due to the low minor allele frequency of this variant and the small number of cases with SSBI, this variant was excluded for association analyses with SSBI after applying the filter on “2 × minor allele frequency × imputation quality × number of cases ≤10.” e491 e492 erlg oue9,Nme aur 9 09Neurology.org/N 2019 29, January | 5 Number 92, Volume | Neurology

Table 2 Follow-up and clinical extension of loci reaching Log10 of Bayesian factor (L10 BF) >4.5 in the discovery stage of the genome-wide association studies

Follow-up (BI, n = 1,452 and Clinical extension ischemic stroke Pathologically defined infarcts SSBI, n = 600) Meta-analysis discovery + follow-up (IS, n = 21,608 and IS-SVD, n = 4,325)a (n = 857)b

Gene (lead SNP) Phenotype OR (95% CI) p Value OR (95% CI) p Value OR (95% CI) p Value OR (95% CI) p Value

− FBN2 (rs39938) BI 0.98 (0.91–1.05) 0.61 1.10 (1.05–1.15) 1.65 × 10 4 1.01 (0.985–1.04) 0.61 1.09 (0.94–1.27) 0.27

− SSBI 1.01 (0.93–1.09) 0.88 1.10 (1.04–1.16) 1.58 × 10 3 1.00 (0.94–1.07) 0.9

PLEKHG1 (rs9371194) BI 0.94 (0.89–0.99) 0.03 1.03 (0.99–1.07) 0.2 1.02 (0.99–1.05) 0.08 0.98 (0.87–1.11) 0.72

SSBI 0.93 (0.88–0.99) 0.02 1.03 (0.99–1.08) 0.17 1.01 (0.96–1.06) 0.75

FRMD1 (rs75889566) BI 1.06 (0.96–1.18) 0.23 0.95 (0.88–1.03) 0.19 0.99 (0.94–1.04) 0.67 1.04 (0.82–1.31) 0.77

SSBI 1.03 (0.92–1.15) 0.61 0.90 (0.82–0.99) 0.03 0.97 (0.87–1.07) 0.52

− LINC00539/ZDHHC20 (rs12583648) BI 0.98 (0.91–1.06) 0.63 1.11 (1.05–1.16) 4.08 × 10 5 1.01 (0.98–1.04) 0.53 0.95 (0.84–1.08) 0.46

− SSBI 0.97 (0.89–1.06) 0.55 1.08 (1.02–1.15) 8.95 × 10 3 1.01 (0.95–1.06) 0.86

− SV2B (rs74587705) BI 1.07 (0.92–1.24) 0.41 1.25 (1.10–1.42) 5.82 × 10 4 1.01 (0.92–1.09) 0.9 1.19 (0.80–1.79) 0.39

SSBI 1.01 (0.86–1.18) 0.92 NA 1.07 (0.91–1.27) 0.41

− CALB2/ZNF23 (rs12373108) BI 1.01 (0.94–1.10) 0.73 1.12 (1.06–1.18) 8.99 × 10 5 1.00 (0.97–1.04) 0.87 0.94 (0.80–1.10) 0.44

− SSBI 1.05 (0.95–1.14) 0.34 1.13 (1.06–1.21) 2.20 × 10 4 1.04 (0.97–1.11) 0.28

Abbreviations: BI = brain infarcts; CI = confidence interval; OR = odds ratio; SNP = single nucleotide polymorphism; SSBI = small subcortical brain infarcts. Values are OR (95% CI) with respect to the minor allele followed by p value of association. a For ischemic stroke, meta-analysis results of METASTROKE-IS, CHARGE-IS, and SiGN-IS are presented. For ischemic stroke due to small vessel disease, meta-analysis results of METASTROKE-IS-SVD, Young Lacunar Stroke DNA Resource-SVD, and SiGN-IS-SVD are presented. b Data presented for the following SNPs are for their proxies: rs75889566 = rs902393 (r2 = 0.96), rs12583648 = rs12584792 (r2 = 0.96), rs74587705 = rs7170681 (r2 = 0.67). for overall IS and IS-SVD was relatively large, it was limited for genetically determined risk for high BP, to be the most sig- pathologically defined BI, and power was insufficient for 4 of nificant modifiable risk factor for BI. No association with the loci (25%–70%) (table e-8, doi.org/10.5061/dryad. cholesterol levels or BMI was found. hk07677). To identify novel genetic risk loci for MRI-defined BI and SSBI, Associations of vascular risk factors with risk of BI or SSBI we have more than doubled the sample size compared to the adjusted for age and sex are presented in table 3 (for un- previously published GWAS of MRI-defined BI,16 used im- adjusted results, see table e-15, doi.org/10.5061/dryad. puted genotypes based on the 1000G reference panel to in- hk07677). Both BI and SSBI were significantly associated crease the marker coverage, and included samples from 5 with all BP indices, the lowest p value being observed for SBP ethnicities for a broader representation of individuals from and MAP. Smoking and diabetes were also associated with different origins. Moreover, we studied both BI and SSBI, while both BI and SSBI. Triglycerides were significantly associated only BI were analyzed in the previously published GWAS meta- with BI only. We did not observe significant associations with analysis.16 Our inability to replicate the genome-wide signifi- levels of HDL cholesterol, LDL cholesterol, BMI, or fasting cant and suggestive findings could reflect false-positive results plasma glucose in nondiabetic participants. Both BI and SSBI but may also be explained by insufficient power in the follow-up were associated with history of cardiovascular disease and stage (table e-8, doi.org/10.5061/dryad.hk07677). Further history of stroke. The most significant association by far was studies on larger samples with MRI-defined BI are required to observed with WMH burden on brain MRI, both for BI and confirm or refute these findings. Moreover, while we could not SSBI. As hypertension is an important risk factor for WMH as provide evidence for an association of genome-wide significant well, we additionally adjusted the regression model for hy- and suggestive risk loci for BI and SSBI with IS, IS-SVD, or pertension to rule out a confounding effect by this variable; pathologically defined BI, this inability could reflect differences however, the association became even more significant (p = in the biology underlying these phenotypes, as well as limited − − 5.71 × 10 172 for BI and p = 4.47 × 10 114 for SSBI) (table power in the extension studies. e-16, doi.org/10.5061/dryad.hk07677). No significant het- erogeneity was seen for these associations across participating The 2 loci that crossed the genome-wide significance studies. threshold, while requiring confirmation in larger independent samples, do harbor plausible biological candidates. Fibrillin2 When exploring the relation of weighted genetic risk scores (FBN2) encodes a protein that is part of the connective tissue for vascular risk factors with BI and SSBI, we found that GRS microfibrils and elastic fiber assembly of the cell.29 Rare and for SBP and MAP were significantly associated with increased common variants in FBN2 have been associated with age- risk of BI and SSBI after correction for multiple testing related macular degeneration.30 Recent studies have also (table 4). In sensitivity analyses using MR-Egger regression, implicated common variants in FBN2 to be associated with evidence for directional pleiotropy was lacking for these SBP,31 although the variants differ (rs6595838-SBP and associations between SBP or MAP GRS and BI or SSBI (p rs39938-BI, r2 = 0.017). The LINC00539/ZDHHC20 locus intercept >0.36). GRS for DBP, BMI, coronary artery disease, was a suggestive hit in a GWAS of adverse metabolic response WMH burden, and IS were nominally associated with BI (p < to hydrochlorothiazide, a drug commonly used to treat hy- 0.05, table 4), but these associations did not survive correction pertension.32 The lead SNP in the region could also influence for multiple testing. the expression of the long noncoding RNA LINC00539 (table e-17, doi.org/10.5061/dryad.hk07677). Discussion Our findings provide definitive evidence for a major and pre- dominant association of increasing BP levels with increased risk This multiethnic meta-analysis comprising over 20,000 of BI and SSBI.1,33 Beside significant associations with hyper- community participants provides noteworthy insight into risk tension, a continuous association was observed for increasing factors for MRI-defined brain infarcts. The described BI dis- levels of all BP measurements (SBP, DBP, PP, MAP), consis- tributions across different age groups and by sex may also tent with elevated BP being the major modifiable risk factor for – serve as a reference for comparison with BI and SSBI fre- BI, as is the case for overt, clinically defined IS.34 36 The im- quency in other settings. Of note, about 90% of BI were portance and causal nature of the relation between high BP and covert, not being associated with a history of stroke. In this risk of BI and SSBI is further supported by the significant multiethnic GWAS of BI and SSBI, we identified 2 genome- association of BP genetic risk scores, for SBP and MAP, with wide significant risk loci for BI, FBN2 on chr5q23 and increased risk of BI, especially SSBI, with no indication of LINC00539/ZDHHC20 on chr13q12, although these could directional pleiotropy using the MR-Egger approach.27 not be replicated in a smaller follow-up sample. We further describe the association of MRI-defined BI with vascular risk Previous publications on the association of BI and SSBI with factors, combining the vast majority of population-based co- vascular risk factors other than elevated BP were hort studies with BI and SSBI measurements available. We inconsistent.1,33,37 Our study provides evidence for a signifi- find high BP, both phenotypically expressed high BP and cant association of current smoking and diabetes with risk of

Neurology.org/N Neurology | Volume 92, Number 5 | January 29, 2019 e493 e494 erlg oue9,Nme aur 9 09Neurology.org/N 2019 29, January | 5 Number 92, Volume | Neurology

Table 3 Association of vascular risk factors with MRI-defined brain infarcts and small subcortical brain infarcts

Brain infarcts Small subcortical brain infarcts

Vascular risk factorsa OR (95% CI) p Value p-het Cases/total, n OR (95% CI) p Value p-het Cases/total, n

Modifiable vascular risk factors

− − Hypertension status 1.62 (1.48–1.78) 9.38 × 10 25b 0.3 3,533/20,555 1.58 (1.40–1.78) 5.23 × 10 14b 0.63 2,015/17,521

− − Systolic blood pressure, mm Hg 1.01 (1.00–1.01) 7.50 × 10 9b 0.26 3,687/19,840 1.01 (1.00–1.01) 3.55 × 10 9b 0.59 2,017/16,816

− − Diastolic blood pressure, mm Hg 1.01 (1.00–1.01) 2.32 × 10 5b 0.74 3,686/19,838 1.01 (1.01–1.02) 1.38 × 10 6b 0.81 2,017/16,815

− − Mean arterial pressure, mm Hg 1.01 (1.01–1.01) 1.35 × 10 8b 0.45 3,686/19,838 1.01 (1.01–1.02) 1.18 × 10 9b 0.77 2,017/16,815

− − Pulse pressure, mm Hg 1.00 (1.00–1.01) 1.07 × 10 5b 0.25 3,686/19,838 1.01 (1.00–1.01) 2.63 × 10 5b 0.42 2,017/16,815

Triglycerides, mmol/L 1.15 (1.05–1.26) 0.0015b 0.31 3,229/16,220 1.15 (1.02–1.28) 0.0163 0.64 1,751/13,374

HDL cholesterol, mmol/L 0.90 (0.84–0.98) 0.0108 0.53 2,590/19,655 0.92 (0.80–1.05) 0.2116 0.14 1,584/16,704

LDL cholesterol, mmol/L 0.96 (0.92–1.01) 0.1441 0.88 3,038/15,449 0.95 (0.89–1.01) 0.1298 0.98 1,644/12,702

BMI, kg/m2 1.00 (0.91–1.01) 0.9515 0.24 2,773/20,509 1.00 (0.99–1.01) 0.5433 0.73 1,511/17,476

− Diabetes status 1.40 (1.24–1.57) 1.66 × 10 8b 0.45 3,259/17,135 1.26 (1.08–1.47) 0.0028b 0.46 1,753/11,836

Fasting plasma glucose, mmol/L 1.00 (1.00–1.00) 0.4366 0.82 3,668/11,599 1.01 (0.99–1.02) 0.2139 0.71 2,007/9,430

− − Current smoking status 1.47 (1.30–1.66) 4.38 × 10 10b 0.29 2,911/15,438 1.37 (1.17–1.62) 1.18 × 10 4b 0.65 1,588/12,032

Vascular comorbidities

− − History of CVD 1.62 (1.46–1.81) 1.03 × 10 18b 0.03 3,202/14,712 1.46 (1.27–1.69) 2.27 × 10 7b 0.12 1,747/12,268

− − History of stroke 5.72 (4.71–6.95) 3.86 × 10 69b 0.61 2,212/1,1374 4.47 (3.35–5.96) 3.15 × 10 24b 0.75 5,89/4,657

− − WMH burden 26.74c 1.43 × 10 157b 3,620/13,499 21.89b 3.16 × 10 106b 1,990/9,917

Abbreviations: BMI = body mass index; CI = confidence interval; CVD = cardiovascular disease; HDL = high-density lipid; LDL = low-density lipid; OR = odds ratio; p-het = p for heterogeneity; WMH = white matter hyperintensity. a All association analyses presented were adjusted for sex and age and meta-analyses estimates presented are from fixed effects inverse variance weighted meta-analyses, except for WMH burden. Association analyses for blood pressure factors were additionally adjusted for usage of blood pressure–lowering drugs. Association analyses for lipid factors were additionally adjusted for usage of lipid-lowering drugs. Association analysis for glucose was performed only on participants without type 2 diabetes. b Associations significant after correcting for the number of independent phenotypes (n = 12, p < 0.0042). c Meta-analyses for WMH burden were performed using sample size weighted meta-analysis, which yielded Z scores as effect estimates and not ORs with CIs; this is because WMH burden was measured on different scales in participating studies (quantitative measures in mL in all but 2 studies and semiquantitative measures on a 10-grade scale in the Atherosclerosis Risk in Communities study and the Cardiovascular Health Study [Additional Methods 1, doi.org/10.5061/dryad.hk07677]). Table 4 Association of genetic risk scores (GRS) for vascular risk factors with brain infarcts and small subcortical brain infarcts

Brain infarcts Small subcortical brain infarcts

Phenotype SNPs, na OR (95% CI) p Value SNPs, na OR (95% CI) p Value

Modifiable vascular risk factors

Systolic blood pressure, GRS-1b 94 1.03 (1.01–1.04) 0.00053c 93 1.03 (1.01–1.05) 0.0014c

Systolic blood pressure, GRS-2d 72 1.03 (1.01–1.05) 0.00036c 71 1.03 (1.01–1.06) 0.0014c

Diastolic blood pressure, GRS-1b 109 1.03 (1.01–1.06) 0.011 109 1.05 (1.01–1.08) 0.0070

Diastolic blood pressure, GRS-2d 71 1.04 (1.01–1.07 0.0142 70 1.05 (1.02–1.09) 0.0057

Pulse pressure, GRS-1b 56 1.02 (0.99–1.05) 0.1234 55 1.01 (0.98–1.05) 0.4724

Pulse pressure, GRS-2d 23 1.03 (1.00–1.06) 0.0695 23 1.02 (0.98–1.07) 0.2969

Mean arterial pressure 30 1.06 (1.02–1.09) 0.0022c 30 1.09 (1.04–1.14) 0.00032c

Triglycerides 38 1.07 (0.89–1.29) 0.4578 37 1.21 (0.95–1.54) 0.1252

HDL cholesterol 71 0.95 (0.82–1.10) 0.4761 71 0.86 (0.71–1.05) 0.1373

LDL cholesterol 52 1.11 (0.96–1.29) 0.1633 52 1.09 (0.90–1.33) 0.3598

Body mass index 76 0.76 (0.59–0.99) 0.0412 76 0.81 (0.58–1.14) 0.2354

Type 2 diabetes 51 1.06 (0.97–1.16) 0.1710 51 1.11 (0.99–1.25) 0.0653

Fasting plasma glucose 36 1.44 (0.96–2.15) 0.0777 36 1.41 (0.83–2.39) 0.2038

Smoking (cigarettes per day) 3 1.00 (0.95–1.05) 0.9921 3 1.07 (1.00–1.14) 0.0624

Smoking (ever vs never smokers) 1 0.96 (0.89–1.03) 0.2769 1 0.95 (0.86–1.05) 0.3007

Smoking (former vs current smokers) 1 1.01 (0.91–1.13) 0.8028 1 1.03 (0.89–1.19) 0.6767

Vascular comorbidities

Ischemic stroke 12 1.49 (1.12–1.97) 0.0057 12 1.36 (0.94–1.97) 0.1011

Coronary artery disease 57 1.12 (1.00–1.25) 0.0441 57 1.08 (0.93–1.24) 0.3169

WMH burden 8 1.41 (1.01–1.96) 0.0416 8 1.49 (0.97–2.30) 0.0702

Abbreviations: CI = confidence interval; HDL = high-density lipoprotein; LDL = low-density lipoprotein; OR = odds ratio; SNP = single nucleotide polymorphism; WMH = white matter hyperintensity. a Number of independent SNPs (r2 < 0.01). b Comprises only risk variants for systolic blood pressure, diastolic blood pressure, and pulse pressure that were previously reported as genome-wide significant and validated in the UK biobank according to prespecified criteria. c Associations significant after correcting for the number of independent phenotypes (n = 12, p < 0.0042). d Comprises, in addition to previously reported variants, all novel variants identified as genome-wide significant for the first time in the UK biobank (Additional methods 4, doi.org/10.5061/dryad.hk07677).

BI and SSBI, while no association with BMI and cholesterol study. Surprisingly, shared genetic variation among the top loci could be demonstrated, despite the very large sample size. for WMH burden and BI was limited. While this observation These findings are consistent with epidemiologic data on IS.35 could be due to lack of power, it could also suggest that WMH Interestingly, in contrast with cholesterol levels, a significant and BI share more environmental than genetic risk factors. A association of increasing triglyceride levels with BI risk was more comprehensive search for shared genetic variation be- observed, although for SSBI the association did not withstand tween WMH burden and BI or SSBI at the genome-wide level correction for multiple testing. Inconsistent results have been using the LD score regression method41 could not be per- reported regarding association of triglycerides with overt, formed in the present study due to low variance in the BI clinically defined IS,38,39 but the present results are in line with GWAS, also hampering the calculation of BI heritability using evidence of an association in older community-dwelling per- the same method. Of note, based on estimates from previously sons between high triglyceride levels and WMH burden, an- published family-based studies, heritability for SSBI was de- other MRI marker of SVD.40 scribed to be low at 29%, in contrast with a moderate to high – heritability for WMH burden at 49%–80%.42 44 Hypertension As previously described, we show a significant association of is a major risk factor for WMH as well, and a BP GRS was also − WMH burden with BI and SSBI, reaching p <10100 in this significantly associated with WMH burden in a prior study.18

Neurology.org/N Neurology | Volume 92, Number 5 | January 29, 2019 e495 However, the association of WMH burden with BI and SSBI (B.M.), and University of Bordeaux (G.C., C.T., M.S., S.K., was still significant after adjusting for hypertension status (table S. Schilling, S.D., B.M.), Department of Neurology, Bordeaux 3), or for SBP levels and BP-lowering treatment (table e-16, doi. University Hospital (S.D.), Bordeaux, France; Departments org/10.5061/dryad.hk07677), suggesting that BP is not the of Epidemiology (H.H.H.A., M.W.V., A.H., M.A.I.), Radiol- only mediator of this association. ogy & Nuclear Medicine (H.H.H.A., M.W.V., A.v.d.L., M.A.I.), Internal Medicine (A.G.U.), and Neurology (M.A.I.), An important strength of the present study is that we have Erasmus MC, Rotterdam, the Netherlands; Department of gathered nearly all large population-based studies with Neurology (C.L.S., V.C., H.J.A., J.R.R., P.M., S. Seshadri), MRI-based identification of BI, genome-wide genotypes, and Boston University School of Medicine; Department of Bio- detailed vascular risk factor and comorbidity assessment, statistics (A.L.D., S. Seshadri), Boston University School of totaling over 20,000 participants covering 5 ethnic groups. Public Health; The National Heart, Lung, and Blood Insti- Despite the unprecedented sample size, we were underpow- tute’s Framingham Heart Study (C.L.S., V.C., A.L.D., H.J.A., ered for the discovery of novel, robust genetic risk loci and J.R.R., P.M., S. Seshadri), MA; Cardiovascular Health Re- even more so for the follow-up of genome-wide significant search Unit, Department of Medicine (J.C.B., B.M.P.), and findings. Our ability to discover robust genetic risk variants Departments of Epidemiology (B.M.P., W.T.L.), Health may also have been hampered by the heterogeneity in BI and Services (B.M.P.), Biostatistics (K. Rice, Q.W.), Neurology SSBI etiology, even though SVD is likely the predominant (W.T.L.), and Pathology (T.J.M.), University of Washington, mechanism,45 and by some heterogeneity in the way BI and Seattle; Pathology (T.J.M.), Standford University, California; SSBI have been measured in participating studies. Finally, Institute for Community Medicine (A. Teumer), Department although the majority of participants had covert BI, 10% had of Psychiatry and Psychotherapy (H.J.G.), Institute of Di- a history of overt, clinically defined stroke, but including both agnostic Radiology and Neuroradiology (N.H.), and De- covert and overt BI also enables a better representation of the partment of Neurology (B.v.S.), University Medicine spectrum of participants with MRI-defined BI in the general Greifswald, Germany; Clinical Division of Neurogeriatrics, population. Whereas history of stroke was more common in Department of Neurology (E.H., R.S.), Institute for Medical participants with BI than those without, we do not believe that Informatics, Statistics and Documentation (E.H.), and Gott- this inclusion has driven the associations we observed, given fried Schatz Research Center (for Cell Signaling, Metabolism both the small number of participants with a stroke history and Aging), Institute of Molecular Biology and Biochemistry and the significance level of the observed associations. (Y.S., H.S.), Medical University of Graz, Austria; Department Moreover, in this population-based setting, determining of Cardiology (S.T., J.W.J.), Section of Gerontology and whether an MRI-defined BI could be attributed to the history Geriatrics, Department of Internal Medicine (S.T.), Molec- of clinically defined stroke was not always possible. ular Epidemiology (P.E.S., M.B., J.D.), Medical Statistics and Bioinformatics (H.-W.U.), and Department of Radiology In clinical practice, MRI-defined BI are commonly seen on (J.v.d.G.), Leiden University Medical Center, the Nether- brain MRI scans performed for various reasons in older per- lands; Department of Pharmacology (S.H., C.C., M.K.I.) and sons. They have been shown to be powerful predictors of in- Department of Ophthalmology, Yong Loo Lin School of cident stroke and incident dementia.1,4,46 Hence BI represent Medicine (C.-Y.C.), National University of Singapore; Ice- an important marker for detection of high-risk individuals and landic Heart Association (A.V.S., V.G.), K´opavogur,Iceland; initiation of preventive interventions. However, no randomized Institute of Molecular Medicine (X.J., M.F.) and Human trials and no recommendations are currently available for the Genetics Center (M.F.), University of Texas Health Science management of covert MRI-defined BI. The observational ev- Center at Houston; Institute for Stroke and Dementia Re- idence is overwhelming for a strong causal relation between search (R.M., M.D.), Klinikum der Universit¨at M¨unchen, high BP and risk of BI and SSBI. A randomized trial will be Ludwig-Maximilians-Universit¨at,Munich, Germany; Clinical needed to decide if persons with MRI-defined BI will benefit Neurosciences (M.T., L.C.A.R.-J., H.S.M.), University of from more intensive BP-lowering strategies than is recom- Cambridge, UK; School of Life Sciences (S.B.), University of mended currently for primary prevention. Lincoln, United Kingdom; German Center for Neurodegen- erative Diseases (DZNE) (L.C.A.R.-J.), Population Health This multiethnic, population-based study on 20,949 partic- Sciences, Bonn, Germany; Department of Medical Genetics ipants sheds important new light on susceptibility factors of (S.L.P.), Department of Neurology, Brain Center Rudolf MRI-defined brain infarcts, a marker of covert vascular brain Magnus (M.K.I.), Department of Epidemiology, Julius Center injury commonly observed in older persons. for Health Sciences and Primary Care (M.I.G., Y.v.d.G.), Department of Genetics, Center for Molecular Medicine (P.I.W.d.B.), and Department of Cardiology, Division Heart Author affiliations & Lungs (F.W.A.), University Medical Center Utrecht, and Utrecht University, the Netherlands; Institut Pasteur de Lille From the Bordeaux Population Health Research Center (V.C., P.A.), Lille University, INSERM, Lille University (G.C., M.S., S.K., S. Schilling, C.T., S.D.), INSERM U1219, Hospital, France; Department of Neurology (Y.-C.Z.), Peking Groupe d'Imagerie Neurofonctionnelle CNRS/CEAU5293 Union Medical College Hospital, Beijing, China; John P. e496 Neurology | Volume 92, Number 5 | January 29, 2019 Neurology.org/N Hussman Institute for Human Genomics (G.W.B., A.H.B., and Mental Health (V.T.), University of Melbourne, Aus- S.H.B.), Department of Neurology (R.L.S., T.R.), Evelyn F. tralia; Centre for Healthy Brain Ageing, Psychiatry (H.B., McKnight Brain Institute (R.L.S., T.R.), Department of Epi- W.W., A. Thalamuthu, N.J.A., E.C., K.A.M., P.S.S.), Dementia demiology and Public Health Sciences (R.L.S., T.R.), and Centre for Research Collaboration (H.B.), and School of Dr. John T. Macdonald Foundation Department of Human Medical Sciences (P.R.S., J.B.K.), University of New South Genetics (R.L.S., S.H.B., A.H.B.), Miller School of Medicine, Wales, Sydney; Mathematics & Statistics (N.J.A.), Murdoch University of Miami, FL; Department of Pathology and University, Perth; Neuroscience Research Australia (K.A.M., Laboratory Medicine (G.D.S.), University of Pennsylvania P.R.S., A.Thalamuthu), Randwick; Brain and Mind Centre School of Medicine, Philadelphia; Departments of Neurology (J.B.K.), The University of Sydney, Camperdown; Queens- & Radiology (O.K.), Landspitali National University Hospi- land Brain Institute (M.J.W.), University of Queensland, tal; Faculty of Medicine (V.G.), University of Iceland, Rey- Brisbane; National Ageing Research Institute (D.A.), Mel- kjavik; Department of Neurology (D.S.K.), Mayo Clinic, bourne; Academic Unit for Psychiatry of Old Age (D.A.), Rochester, MN; Departments of Data Science (M.E.G.) and University of Melbourne, Australia; Department of Cere- Medicine (B.G.W., T.H.M.), University of Mississippi Med- brovascular Diseases (G.B.B.), Fondazione IRCCS Istituto ical Center, Jackson; Department of Neurology (R.F.G.), Neurologico “Carlo Besta,” Milan, Italy; CTSU, Nuffield Johns Hopkins University School of Medicine, Baltimore, Department of Population Health (J.C.H., C.M.v.D.), and MD; Department of Gene Diagnostics and Therapeutics Nuffield Department of Clinical Neurosciences (P.M.R.), (F.T., N.K.), Research Institute, National Center for Global University of Oxford, UK; National Institute of Neurological Health and Medicine, Tokyo; The Third Department of In- Disorders and Stroke (C.B.W.), NIH, Bethesda, MD; In- ternal Medicine (S.Y.) and Department of Functional Pa- stitute of Cardiovascular and Medical Sciences, Faculty of thology (T.N.), Shimane University School of Medicine, Medicine (D.J.S.), and Robertson Centre for Biostatistics Japan; Rush University Medical Center (K.B.R., N.T.A., (I.F.), University of Glasgow, UK; German Center for Neu- D.A.E.), Chicago, IL; Brigham and Women’s Hospital (K. rodegenerative Diseases (DZNE) (K.W., H.J.G.), Site Rexrode); Center for Translational & Computational Neu- Rostock, Greifswald, Germany; Interfaculty Institute for Ge- roimmunology, Department of Neurology (P.L.D.J.), Co- netics and Functional Genomics (U.V.), University of lumbia University Medical Center, New York, NY; Kaiser Greifswald, Germany; John Hunter Hospital (C.L.), Hunter Permanente Washington Health Research Institute (B.M.P.), Medical Research Institute and University of Newcastle, Seattle, WA; Institute for Translational Genomics and Pop- Callaghan, Australia; Neurovascular Research Group (NEU- ulation Sciences, Los Angeles Biomedical Research Institute, VAS) (J.J.-C.), Neurology Department, IMIM–Hospital del and Division of Genomic Outcomes, Department of Pediat- Mar, Barcelona, Spain; Institute of Cardiovascular Research rics (J.I.R.), Harbor-UCLA Medical Center, Torrance; (P.S.), Royal Holloway University of London & St Peters and Departments of Pediatrics, Medicine, and Human Genetics Ashford Hospital, UK; Center for Human Genetic Research (J.I.R.), UCLA, Los Angeles, CA; Department of Neurology and Department of Neurology (J.R.), Program in Medical and (O.L.L.), University of Pittsburgh, PA; Singapore Eye Re- Population Genetics, Broad Institute, Massachusetts General search Institute (J.L.); Duke-NUS Graduate Medical School Hospital, Harvard Medical School, Boston; University of (C.-.YC., T.Y.W., M.K.I.), Singapore; Singapore Eye Research Cincinnati College of Medicine (D.W.), OH; Department of Institute (C.-.YC., T.Y.W., M.K.I.), Singapore National Eye Neurology (J.W.C., S.J.K.), University of Maryland School of Centre; Memory Aging & Cognition Centre (MACC), Na- Medicine and Baltimore VAMC; Department of Neurology tional University Health System (M.K.I.), Singapore; Genetic (J.F.M.), Mayo Clinic Jacksonville, FL; Department of Neu- Epidemiology Unit, Department of Epidemiology and Bio- rology (A.S.), Jagiellonian University, Krakow, Poland; De- statistics (S.J.v.d.L., N.A., C.M.v.D.), Erasmus MC University partment of Clinical Sciences Lund, Neurology (A.L.), Lund Medical Center, Rotterdam, the Netherlands; Department of University; Department of Neurology and Rehabilitation Neurology (C.D.), University of California at Davis; Brain Medicine (A.L.), Skåne University Hospital; Department of Research Imaging Centre (J.M.W., M.d.C.V.H., M.E.B., Clinical Sciences Malm¨o(O.M.), Lund University, Sweden; S.M.M.), Centre for Clinical Brain Sciences (J.M.W., Neuroscience Institute (R.P.G.), Saint Francis Medical Cen- M.d.C.V.H., M.E.B., C.L.M.S.), Edinburgh Dementia Re- ter, School of Health and Medical Sciences, Seton Hall Uni- search Centre (J.M.W., M.d.C.V.H.), Centre for Cognitive versity, South Orange, NJ; College of Public Health (D.K.A.), Ageing and Cognitive Epidemiology (J.M.W., M.d.C.V.H., University of Kentucky, Lexington; Institute of Biomedicine M.L., D.L., I.J.D., M.E.B., J.M.S., S.M.M.), Alzheimer Scotland (C.J.), the Sahlgrenska Academy at University of Gothenburg, Dementia Research Centre (J.M.S.), and Institute of Genetics Sweden; Department of Pharmacotherapy and Translational and Molecular Medicine (C.L.M.S.), University of Edinburgh, Research and Center for Pharmacogenomics, College of UK; Department of Neurosciences, Experimental Neurology Pharmacy (J.A.J.), and Division of Cardiovascular Medicine, and Leuven Research Institute for Neuroscience and Disease College of Medicine (J.A.J.), University of Florida, Gaines- (LIND) (R.L.), KU Leuven–University of Leuven; Center for ville; Department of Neurology (O.R.B.), University of Brit- Brain & Disease Research (R.L.), VIB, Laboratory of Neu- ish Columbia, Vancouver, Canada; Department of robiology; Department of Neurology (R.L.), University Epidemiology and Population Health (S.W.-S.), Albert Ein- Hospitals Leuven, Belgium; Florey Institute of Neuroscience stein College of Medicine, Bronx, NY; Stroke Center,

Neurology.org/N Neurology | Volume 92, Number 5 | January 29, 2019 e497 Department of Neurology (J.-M.L.), Washington University Wright, Boncoraglio, Wen, Thalamuthu, Armstrong, van der School of Medicine, St. Louis, MO; Department of Medicine Grond, Stott, Ford, Jukema, van der Lugt, Wittfeld, Grabe, (B.D.M., P.F.M.), University of Maryland School of Medicine, Hosten, von Sarnowski, Jimenez-Conde, Rosand, Thijs, Baltimore; Center for Public Health Genomics (S.S.R.), Lindgren, Rundek, Lee, Mitchell, Rich, Geerlings, McWhirter, University of Virginia School of Medicine, Charlottesville; Callisaya, Mather, Sachdev, Dichgans, Kittner, Markus, Lau- Durrer Center for Cardiovascular Research (F.W.A.), Neth- ner, Seshadri, Longstreth, Debette, Adams, Bis, Jian, Pulit, erlands Heart Institute, Utrecht, the Netherlands; Institute of Amouyel, Mazoyer, Zhu, Sargurupremraj, Kaffashian, Bee- Cardiovascular Science, Faculty of Population Health Scien- cham, Montine, Kjartansson, Gudnason, Gottesman, Rajan, ces (F.W.A.), and Farr Institute of Health Informatics Re- Aggarwal, De Jager, Evans, Rotter, Liao, Wong, Ikram, search and Institute of Health Informatics (F.W.A.), Chouraki, DeStefano, Romero, Maillard, Wardlaw, Ames, University College London, UK; Peninsula Clinical School Vernooij, Hofman, Uitterlinden, Levi, Sudlow, Woo, Meschia, (V.S., C.M., M.C.), Frankston Hospital, Central Clinical Slowik, Melander, Grewal, Rothwell, Jern, van der Graaf, de School, and School of Clinical Sciences (T.P.), Monash Bakker, Asselbergs, Thomson, Rutten-Jacobs, Bevan, Worrall, Health, Monash University, Melbourne; Menzies Institute for Ikram, Smith, and Schilling. Funding: Drs. Schofield, Trompet, Medical Research (V.S., R.M., M.C.), University of Tasmania, Tzourio, Schellenberg, Mosley, Schmidt, Schmidt, Psaty, Hobart; Western Sydney University (R.T.), New South Cheng, Deary, Starr, Bastin, Slagboom, Wright, Wen, Stott, Wales, Australia; Departments of Neurology and Public Ford, Jukema, V¨olker, Rosand, Lindgren, Johnson, Srikanth, Health Sciences (B.B.W.), University of Virginia, Charlot- Mather, Sachdev, Markus, Fornage, Seshadri, Longstreth, and tesville; Neuropsychiatric Institute (P.S.S.), Prince of Wales Debette. Supervision: Drs. Kwok, Schellenberg, Psaty, Amin, Hospital, Randwick, Australia; Munich Cluster for Systems Slagboom, Wen, Jukema, Grabe, Mather, Sachdev, van Duijn, Neurology (SyNergy) (M.D.), Munich, Germany; Intramural Fornage, Seshadri, Longstreth, and Debette. Final approval of Research Program (L.J.L.), National Institute on Aging, NIH, the version to be published: Drs. Satizabal, Teumer, Hofer, Bethesda, MD; and University of Texas Health Sciences Trompet, Hilal, Malik, Traylor, Tzourio, Schellenberg, Gris- Center and Glenn Biggs Institute for Alzheimer’s and Neu- wold, Windham, Mosley, Schmidt, Schmidt, Takeuchi, rodegenerative Diseases (C.L.S., S. Seshadri), San Antonio. Nabika, Kato, Psaty, Rice, Lopez, Chen, Cheng, van der Lee, Amin, Aparicio, DeCarli, del Carmen Vald´es Hern´andez, Luciano, Deary, Starr, Bastin, Maniega, Slagboom, Beekman, Author contributions Deelen, Uh, Lemmens, Brodaty, Wright, Boncoraglio, Hope- Drs. Chauhan, Adams, Satizabal, Bis, Teumer, and Sargur- well, Beecham, Wen, Thalamuthu, Armstrong, Chong, van der upremraj contributed equally to this work. Drs. Ikram, For- Grond, Stott, Ford, Jukema, van der Lugt, Wittfeld, Grabe, nage, Launer, Seshadri, Longstreth, and Debette jointly Hosten, von Sarnowski, V¨olker, Jimenez-Conde, Rosand, supervised this work. Study design/conception: Drs. Scho- Cole, Thijs, Lindgren, Rundek, Rexrode, Johnson, field, Tzourio, Schellenberg, Kato, Psaty, Slagboom, Stott, Wasssertheil-Smoller, Wong, Mitchell, Rich, McArdle, Geerl- Ford, Jukema, Fornage, Seshadri, Longstreth, and Debette. ings, McWhirter, Moran, Callisaya, Mather, Sachdev, van Statistical analysis: Drs. Schofield, Chauhan, Sargurupremraj, Duijn, Dichgans, Markus, Fornage, Launer, Seshadri, Long- Satizabal, Teumer, Hofer, Trompet, Hilal, Smith, Malik, Saba, streth, Debette, Kittner, Adams, Bis, Jian, Pulit, Amouyel, Takeuchi, Rice, van der Lee, Luciano, Deelen, Uh, Beecham, Mazoyer, Zhu, Sargurupremraj, Kaffashian, Beecham, Mon- Thalamuthu, Armstrong, Grabe, and McWhirter. Sample/ tine, Kjartansson, Gudnason, Gottesman, Rajan, Aggarwal, De phenotype contribution: Drs. Phan, Schofield, Kwok, Hilal, Jager, Evans, Rotter, Liao, Wong, Ikram, Chouraki, DeStefano, Malik, Traylor, Tzourio, Schellenberg, Knopman, Griswold, Romero, Maillard, Wardlaw, Ames, Vernooij, Hofman, Uit- Windham, Mosley, Schmidt, Schmidt, Yamaguchi, Nabika, terlinden, Levi, Sudlow, Woo, Meschia, Slowik, Melander, Psaty, Lopez, Chen, Cheng, Amin, Aparicio, DeCarli, del Grewal, Rothwell, Jern, der Graaf, de Bakker, Asselbergs, Carmen Vald´esHern´andez, Deary, Starr, Bastin, Maniega, Thomson, Rutten-Jacobs, Bevan, Worrall, Ikram, and Smith. Beekman, Lemmens, Brodaty, Wright, Boncoraglio, Hopewell, Blanton, Wright, Sacco, Wen, Chong, van der Grond, Stott, Acknowledgment Ford, Jukema, van der Lugt, Wittfeld, Grabe, Hosten, von The authors thank Dr. Anton J.M. de Craen (September Sarnowski, V¨olker, Jimenez-Conde, Sharma, Rosand, Cole, 1966–January 2016) from the PROSPER and LLS study for Thijs, Lindgren, Rundek, Rexrode, Arnett, Johnson, Bena- his contribution to this work; the Older Australian Twins vente, Wasssertheil-Smoller, Lee, Mitchell, McArdle, Moran, Study (OATS) participants, their supporters, and the OATS Callisaya, Mather, Sachdev, van Duijn, Dichgans, Kittner, Research Team; all study participants and their relatives, Markus, Launer, Longstreth, and Debette. Drafting the article: general practitioners, and neurologists for their contributions; Drs. Chauhan, Seshadri, Longstreth, and Debette. Critical P. Veraart for help in genealogy; J. Vergeer for supervision of revision of the article: Drs. Chauhan, Satizabal, Teumer, Hofer, the laboratory work; P. Snijders for help in data collection; Dr. Trompet, Malik, Traylor, Tzourio, Windham, Mosley, Anne Boland (CNG) for technical help in preparing the DNA Schmidt, Yamaguchi, Kato, Psaty, Rice, Lopez, Chen, Aparicio, samples for analyses; the University of Newcastle for funding; DeCarli, del Carmen Vald´es Hern´andez, Luciano, Deary, and the men and women of the Hunter region who participated Bastin, Maniega, Slagboom, Beekman, Deelen, Uh, Lemmens, in this study. e498 Neurology | Volume 92, Number 5 | January 29, 2019 Neurology.org/N Study funding a NHMRC grant (ID1045325). OATS was facilitated via CHAP: R01-AG-11101, R01-AG-030146, NIRP-14-302587. access to the Australian Twin Registry, which is supported by SMART: This study was supported by a grant from the the NHMRC Enabling Grant 310667. The OATS genotyping Netherlands Organization for Scientific Research–Medical was partly supported by a Commonwealth Scientific and In- Sciences (project no. 904-65–095). LBC: The authors thank dustrial Research Organisation Flagship Collaboration Fund the LBC1936 participants and the members of the LBC1936 Grant. NOMAS: The Northern Manhattan Study is funded research team who collected and collated the phenotypic and by the NIH grant “Stroke Incidence and Risk Factors in a Tri- genotypic data. The LBC1936 is supported by Age UK Ethnic Region” (NINDS R01NS 29993). TASCOG: (Disconnected Mind Programme grant). The work was NHMRC and Heart Foundation. AGES: The study was undertaken by The University of Edinburgh Centre for funded by the National Institute on Aging (NIA) (N01-AG- Cognitive Ageing and Cognitive Epidemiology, part of the 12100), Hjartavernd (the Icelandic Heart Association), and cross-council Lifelong Health and Wellbeing Initiative (MR/ the Althingi (the Icelandic Parliament), with contributions K026992/1). The brain imaging was performed in the Brain from the Intramural Research Programs at the NIA, the Na- Research Imaging Centre (https://www.ed.ac.uk/clinical- tional Heart, Lung, and Blood Institute (NHLBI), and the sciences/edinburgh-imaging), a center in the SINAPSE Col- National Institute of Neurological Disorders and Stroke laboration (sinapse.ac.uk) supported by the Scottish Funding (NINDS) (Z01 HL004607-08 CE). ERF: The ERF study as Council and Chief Scientist Office. Funding from the UK a part of European Special Populations Research Network Biotechnology and Biological Sciences Research Council (EUROSPAN) was supported by European Commission FP6 (BBSRC) and the UK Medical Research Council is ac- STRP grant no. 018947 (LSHG-CT-2006-01947) and also received funding from the European Community’s Seventh knowledged. Genotyping was supported by a grant from the – BBSRC (ref. BB/F019394/1). PROSPER: The PROSPER Framework Programme (FP7/2007 2013)/grant agreement HEALTH-F4-2007-201413 by the European Commission study was supported by an investigator-initiated grant under the programme “Quality of Life and Management of obtained from Bristol-Myers Squibb. Prof. Dr. J.W. Jukema is the Living Resources” of 5th Framework Programme (no. an Established Clinical Investigator of the Netherlands Heart QLG2-CT-2002-01254). High-throughput analysis of the Foundation (grant 2001 D 032). Support for genotyping was ERF data was supported by a joint grant from Netherlands provided by the seventh framework program of the European Organization for Scientific Research and the Russian Foun- commission (grant 223004) and by the Netherlands dation for Basic Research (NWO-RFBR 047.017.043). Genomics Initiative (Netherlands Consortium for Healthy Exome sequencing analysis in ERF was supported by the Aging grant 050-060-810). SCES and SiMES: National ZonMw grant (project 91111025). Najaf Amin is supported Medical Research Council Singapore Centre Grant NMRC/ by the Netherlands Brain Foundation (project no. F2013[1]- CG/013/2013. C.-Y.C. is supported by the National Medical 28). ARIC: The Atherosclerosis Risk in Communities study Research Council, Singapore (CSA/033/2012), Singapore was performed as a collaborative study supported by NHLBI Translational Research Award (STaR) 2013. Dr. Kamran contracts (HHSN268201100005C, HSN268201100006C, Ikram received additional funding from the Singapore Min- HSN268201100007C, HHSN268201100008C, HHSN268- ’ istry of Health s National Medical Research Council 201100009C, HHSN268201100010C, HHSN268201100011C, (NMRC/CSA/038/2013). SHIP: SHIP is part of the Com- and HHSN268201100012C), R01HL70825, R01HL087641, munity Medicine Research net of the University of Greifs- R01HL59367, and R01HL086694; National Human Genome wald, Germany, which is funded by the Federal Ministry of Research Institute contract U01HG004402; and NIH contract Education and Research (grants no. 01ZZ9603, 01ZZ0103, HHSN268200625226C. Infrastructure was partly supported by ff and 01ZZ0403), the Ministry of Cultural A airs, as well as the grant no. UL1RR025005, a component of the NIH and NIH – Social Ministry of the Federal State of Mecklenburg West Roadmap for Medical Research. This project was also supported “ Pomerania, and the network Greifswald Approach to In- by NIH R01 grant NS087541 to M.F. FHS: This work was ” dividualized Medicine (GANI_MED) funded by the Federal supported by the National Heart, Lung and Blood Institute’s Ministry of Education and Research (grant 03IS2061A). Framingham Heart Study (contracts no. N01-HC-25195 and no. Genome-wide data have been supported by the Federal HHSN268201500001I), and its contract with Affymetrix, Inc. for Ministry of Education and Research (grant no. 03ZIK012) genotyping services (contract no. N02-HL-6-4278). A portion of and a joint grant from Siemens Healthineers, Erlangen, Ger- this research utilized the Linux Cluster for Genetic Analysis many, and the Federal State of Mecklenburg–West Pomer- (LinGA-II) funded by the Robert Dawson Evans Endowment of ania. Whole-body MRI was supported by a joint grant from the Department of Medicine at Boston University School of Siemens Healthineers, Erlangen, Germany, and the Federal Medicine and Boston Medical Center. This study was also sup- State of Mecklenburg–West Pomerania. The University of ported by grants from the NIA (R01s AG033040, AG033193, Greifswald is a member of the Cach´eCampus program of the AG054076, AG049607, AG008122, and U01-AG049505) and InterSystems GmbH. OATS (Older Australian Twins Study): the NINDS (R01-NS017950, UH2 NS100605). Dr. DeCarli is OATS was supported by an Australian National Health and supported by the Alzheimer’s Disease Center (P30 AG 010129). Medical Research Council (NHRMC)/Australian Research ASPS: The research reported in this article was funded by the Council (ARC) Strategic Award (ID401162) and by Austrian Science Fund (FWF) grant nos. P20545-P05, P13180,

Neurology.org/N Neurology | Volume 92, Number 5 | January 29, 2019 e499 and P20545-B05, by the Austrian National Bank Anniversary initiated research funding from the French National Research Fund, P15435, and the Austrian Ministry of Science under the Agency (ANR) and from the Fondation Leducq. S.D. is sup- aegis of the EU Joint Programme–Neurodegenerative Disease ported by a starting grant from the European Research Council Research (JPND) (jpnd.eu). LLS: The Leiden Longevity Study (SEGWAY), a grant from the Joint Programme of Neurode- has received funding from the European Union’s Seventh generative Disease research (BRIDGET), from the European Framework Programme (FP7/2007–2011) under grant agree- Union’s Horizon 2020 research and innovation programme un- ment no. 259679. This study was supported by a grant from the der grant agreements No 643417 & No 640643, and by the Innovation-Oriented Research Program on Genomics (Senter- Initiative of Excellence of Bordeaux University. Part of the Novem IGE05007), the Centre for Medical Systems Biology, and computations were performed at the Bordeaux Bioinformatics the Netherlands Consortium for Healthy Ageing (grant 050-060- Center (CBiB), University of Bordeaux. This work was sup- 810), all in the framework of the Netherlands Genomics Initia- ported by the National Foundation for Alzheimer’sDiseaseand tive, Netherlands Organization for ScientificResearch(NWO), Related Disorders, the Institut Pasteur de Lille, the Labex DIS- UnileverColworth, and by BBMRI-NL, a Research Infrastruc- TALZ, and the Centre National de G´enotypage. ADGC: The ture financed by the Dutch government (NWO 184.021.007). Alzheimer Disease Genetics Consortium is supported by NIH. CHS: This CHS research was supported by NIH-NIA supported this work through the following grants: contracts HHSN268201200036C, HHSN268200800007C, ADGC, U01 AG032984, RC2 AG036528; NACC, U01 N01HC55222, N01HC85079, N01HC85080, N01HC85081, AG016976; NCRAD, U24 AG021886; NIA LOAD, U24 N01HC85082, N01HC85083, N01HC85086, N01HC15103, AG026395, U24 AG026390; Banner Sun Health Research In- and HHSN268200960009C and grants U01HL080295, stitute, P30 AG019610; Boston University, P30 AG013846, U01 R01HL087652, R01HL105756, R01HL103612, R01HL120393, AG10483, R01 CA129769, R01 MH080295, R01 AG017173, R01HL085251, and R01HL130114 from the NHLBI with ad- R01 AG025259, R01AG33193; Columbia University, P50 ditional contribution from NINDS. Additional support was AG008702, R37 AG015473; Duke University, P30 AG028377, provided through R01AG023629 from the NIA. A full list of AG05128; Emory University, AG025688; Group Health Re- principal CHS investigators and institutions can be found at search Institute, UO1 AG06781, UO1 HG004610; Indiana CHS-NHLBI.org. The provision of genotyping data was sup- University, P30 AG10133; Johns Hopkins University, P50 ported in part by the National Center for Advancing Trans- AG005146, R01 AG020688; Massachusetts General Hospital, lational Sciences, CTSI grant UL1TR001881, and the National P50 AG005134; Mayo Clinic, P50 AG016574; Mount Sinai Institute of Diabetes and Digestive and Kidney Disease Diabetes School of Medicine, P50 AG005138, P01 AG002219; New York Research Center grant DK063491 to the Southern California University, P30 AG08051, MO1RR00096, UL1 RR029893, Diabetes Endocrinology Research Center. The content is solely 5R01AG012101, 5R01AG022374, 5R01AG013616, the responsibility of the authors and does not necessarily repre- 1RC2AG036502, 1R01AG035137; Northwestern University, sent the official views of the NIH. Rotterdam Study: The gen- P30 AG013854; Oregon Health & Science University, P30 eration and management of GWAS genotype data for the AG008017, R01 AG026916; Rush University, P30 AG010161, Rotterdam Study is supported by the Netherlands Organisation R01 AG019085, R01 AG15819, R01 AG17917, R01 AG30146; of Scientific Research (NWO) Investments (no. 175.010.2005. TGen, R01 NS059873; University of Alabama at Birmingham, 011, 911-03-012). This study is funded by the Research Institute P50 AG016582, UL1RR02777; University of Arizona, R01 for Diseases in the Elderly (014-93-015; RIDE2), the Nether- AG031581; University of California, Davis, P30 AG010129; lands Genomics Initiative (NGI)/NWO project no. 050-060- University of California, Irvine, P50 AG016573, P50, P50 810. The Rotterdam Study is funded by Erasmus MC Medical AG016575, P50 AG016576, P50 AG016577; University of Cal- Center and Erasmus MC University, Rotterdam, Netherlands ifornia, Los Angeles, P50 AG016570; University of California, Organization for Health Research and Development (ZonMw), San Diego, P50 AG005131; University of California, San Fran- the Research Institute for Diseases in the Elderly (RIDE), the cisco, P50 AG023501, P01 AG019724; University of Kentucky, Ministry of Education, Culture and Science, the Ministry for P30 AG028383, AG05144; University of Michigan, P50 Health, Welfare and Sports, the European Commission (DG AG008671; University of Pennsylvania, P30 AG010124; Uni- XII), and the Municipality of Rotterdam. M.A.I. is supported by versity of Pittsburgh, P50 AG005133, AG030653; University of an NWO Veni grant (916.13.054). The 3-City Study: The 3-City Southern California, P50 AG005142; University of Texas Study is conducted under a partnership agreement among the Southwestern, P30 AG012300; University of Miami, R01 Institut National de la Sant´e et de la Recherche M´edicale AG027944, AG010491, AG027944, AG021547, AG019757; (INSERM), the University of Bordeaux, and Sanofi-Aventis. The University of Washington, P50 AG005136; Vanderbilt Univer- Fondation pour la Recherche M´edicale funded the preparation sity, R01 AG019085; and Washington University, P50 and initiation of the study. The 3C Study is also supported by the AG005681, P01 AG03991. The Kathleen Price Bryan Brain Bank Caisse Nationale Maladie des Travailleurs Salari´es, Direction at Duke University Medical Center is funded by NINDS grant G´en´eraledelaSant´e, Mutuelle G´en´erale de l’Education Natio- NS39764, NIMH MH60451, and by GlaxoSmithKline. Geno- nale (MGEN), Institut de la Long´evit´e, Conseils R´egionaux of typing of the TGEN2 cohort was supported by Kronos Science. Aquitaine and Bourgogne, Fondation de France, and Ministry of The TGen series was also funded by NIA grant AG041232, the Research–INSERM Programme “Cohortes et collections de Banner Alzheimer’s Foundation, The Johnnie B. Byrd Sr. Alz- donn´ees biologiques.” C.T. and S.D. have received investigator- heimer’s Institute, the Medical Research Council, and the state of e500 Neurology | Volume 92, Number 5 | January 29, 2019 Neurology.org/N Arizona and also includes samples from the following sites: the Vincent Fairfax Family Foundation in Australia. E.G.H. was Newcastle Brain Tissue Resource (funding via the Medical Re- supported by a Fellowship from the NHF and National Stroke search Council [MRC], local NHS trusts, and Newcastle Uni- Foundation of Australia (ID: 100071). J.M. was supported by an versity), MRC London Brain Bank for Neurodegenerative Australian Postgraduate Award. BRAINS: Bio-Repository of Diseases (funding via the Medical Research Council), South DNA in Stroke (BRAINS) is partly funded by a Senior Fellow- West Dementia Brain Bank (funding via numerous sources in- ship from the Department of Health (UK) to P.S., the Henry cluding the Higher Education Funding Council for England Smith Charity, and the UK-India Education Research Institutive [HEFCE], Alzheimer’s Research Trust [ART], BRACE, as well (UKIERI) from the British Council. GEOS: Genetics of Early as North Bristol NHS Trust Research and Innovation De- Onset Stroke (GEOS) Study, Baltimore, was supported by GEI partment and DeNDRoN), The Netherlands Brain Bank Grant U01 HG004436, as part of the GENEVA consortium (funding via numerous sources including Stichting MS Research, under GEI, with additional support provided by the Mid-Atlantic Brain Net Europe, Hersenstichting Nederland Breinbrekend Nutrition and Obesity Research Center (P30 DK072488), and Werk, International Parkinson Fonds, Internationale Stiching the Office of Research and Development, Medical Research Alzheimer Onderzoek), Institut de Neuropatologia, Servei Service, and the Baltimore Geriatrics Research, Education, and Anatomia Patologica, and Universitat de Barcelona). ADNI: Clinical Center of the Department of Veterans Affairs. Geno- Funding for ADNI is through the Northern California Institute typing services were provided by the Johns Hopkins University for Research and Education by grants from Abbott, AstraZeneca Center for Inherited Disease Research (CIDR), which is fully AB, Bayer Schering Pharma AG, Bristol-Myers Squibb, Eisai funded through a federal contract from the NIH to the Johns Global Clinical Development, Elan Corporation, Genentech, GE Hopkins University (contract no. HHSN268200782096C). As- Healthcare, GlaxoSmithKline, Innogenetics, Johnson & Johnson, sistance with data cleaning was provided by the GENEVA Co- Eli Lilly and Co., Medpace, Inc., Merck and Co., Inc., Novartis ordinating Center (U01 HG 004446; PI Bruce S. Weir). Study AG, PfizerInc,F.Hoffman-La Roche, Schering-Plough, Synarc, recruitment and assembly of datasets were supported by a Co- Inc., Alzheimer’s Association, Alzheimer’s Drug Discovery operative Agreement with the Division of Adult and Community Foundation, the Dana Foundation, and the National Institute of Health, Centers for Disease Control and Prevention, and by Biomedical Imaging and Bioengineering and NIA grants U01 grants from NINDS and the NIH Office of Research on AG024904, RC2 AG036535, and K01 AG030514. Support was Women ’s Health (R01 NS45012, U01 NS069208-01). HPS: also provided by the Alzheimer’s Association (LAF, IIRG-08- Heart Protection Study (HPS) (ISRCTN48489393) was sup- 89720; MAP-V, IIRG-05-14147) and the US Department of ported by the UK MRC, British Heart Foundation, Merck and Veterans Affairs Administration, Office of Research and De- Co. (manufacturers of simvastatin), and Roche Vitamins Ltd. velopment, Biomedical Laboratory Research Program. SiGN: (manufacturers of vitamins). Genotyping was supported by Stroke Genetic Network (SiGN) was supported in part by award a grant to Oxford University and CNG from Merck and Co. nos. U01NS069208 and R01NS100178 from NINDS. Genetics J.C.H. acknowledges support from the British Heart Foundation of Early-Onset Stroke (GEOS) Study was supported by the NIH (FS/14/55/30806). ISGS: Ischemic Stroke Genetics Study Genes, Environment and Health Initiative (GEI) grant U01 (ISGS)/Siblings With Ischemic Stroke Study (SWISS) was HG004436, as part of the GENEVA consortium under GEI, with supported in part by the Intramural Research Program of the additional support provided by the Mid-Atlantic Nutrition and NIA, NIH project Z01 AG-000954-06. ISGS/SWISS used Obesity Research Center (P30 DK072488); and the Office of samples and clinical data from the NIH-NINDS Human Ge- Research and Development, Medical Research Service, and the netics Resource Center DNA and Cell Line Repository (ccr. Baltimore Geriatrics Research, Education, and Clinical Center of coriell.org/ninds), human subjects protocol nos. 2003-081 and the Department of Veterans Affairs. Genotyping services were 2004-147. ISGS/SWISS used stroke-free participants from the provided by the Johns Hopkins University Center for Inherited Baltimore Longitudinal Study of Aging (BLSA) as controls. The Disease Research (CIDR), which is fully funded through a federal inclusion of BLSA samples was supported in part by the Intra- contract from the NIH to Johns Hopkins University (contract no. mural Research Program of the NIA, NIH project Z01 AG- HHSN268200782096C). Assistance with data cleaning was 000015-50, human subjects protocol no. 2003-078. The ISGS provided by the GENEVA Coordinating Center (U01 HG study was funded by NIH-NINDS Grant R01 NS-42733 (J.F.M. 004446; PI Bruce S. Weir). Study recruitment and assembly of ). The SWISS study was funded by NIH-NINDS Grant R01 NS- datasets were supported by a Cooperative Agreement with the 39987 (J.F.M.). This study used the high-performance compu- Division of Adult and Community Health, Centers for Disease tational capabilities of the Biowulf Linux cluster at the NIH Control and Prevention, and by grants from NINDS and the (biowulf.nih.gov). MGH-GASROS: MGH Genes Affecting NIH Office of Research on Women’s Health (R01 NS45012, Stroke Risk and Outcome Study (MGH-GASROS) was sup- U01 NS069208-01). METASTROKE: ASGC: Australian pop- ported by NINDS (U01 NS069208), the American Heart ulation control data were derived from the Hunter Community Association/Bugher Foundation Centers for Stroke Prevention Study. This research was funded by grants from the Australian Research 0775010N, the NIH and NHLBI’sSTAMPEED National and Medical Health Research Council (NHMRC genomics research program (R01 HL087676), and a grant from Project Grant ID: 569257), the Australian National Heart the National Center for Research Resources. The Broad Institute Foundation (NHF Project Grant ID: G 04S 1623), the Univer- Center for Genotyping and Analysis is supported by grant U54 sity of Newcastle, the Gladys M Brawn Fellowship scheme, and RR020278 from the National Center for Research resources.

Neurology.org/N Neurology | Volume 92, Number 5 | January 29, 2019 e501 Milan: Milano–Besta Stroke Register Collection and genotyping maintained and supported by the United States National Center of the Milan cases within CEDIR were supported by the Italian for Biotechnology Information, US National Library of Medicine. Ministry of Health (grant nos.: RC 2007/LR6, RC 2008/LR6; WHI: Funding support for WHI-GARNET was provided RC 2009/LR8; RC 2010/LR8; GR-2011-02347041), FP6 through the NHGRI GARNET (grant no. U01 HG005152). LSHM-CT-2007-037273 for the PROCARDIS control samples. Assistance with phenotype harmonization and genotype cleaning, WTCCC2: Wellcome Trust Case-Control Consortium 2 as well as with general study coordination, was provided by the (WTCCC2) was principally funded by the Wellcome Trust, as GARNET Coordinating Center (U01 HG005157). Funding part of the Wellcome Trust Case Control Consortium 2 project support for genotyping, which was performed at the Broad In- (085475/B/08/Z and 085475/Z/08/Z and WT084724MA). stitute of MIT and Harvard, was provided by the GEI (U01 The Stroke Association provided additional support for collection HG004424). R.L. is a senior clinical investigator of FWO Flan- of some of the St George’s, London cases. The Oxford cases were ders. F.W.A. is supported by a Dekker scholarship-Junior Staff collected as part of the Oxford Vascular Study, which is funded by Member 2014T001–Netherlands Heart Foundation and UCL the MRC, Stroke Association, Dunhill Medical Trust, National Hospitals NIHR Biomedical Research Centre. Institute of Health Research (NIHR), and the NIHR Biomedical Research Centre, Oxford. The Edinburgh Stroke Study was Disclosure supported by the Wellcome Trust (clinician scientist award to The authors report no disclosures relevant to the manuscript. C.L.M.S.) and the Binks Trust. Sample processing occurred in the Go to Neurology.org/N for full disclosures. Genetics Core Laboratory of the Wellcome Trust Clinical Re- search Facility, Western General Hospital, Edinburgh. Much of Publication history the neuroimaging occurred in the Scottish Funding Council Received by Neurology February 20, 2018. Accepted in final form Brain Imaging Research Centre (https://www.ed.ac.uk/clinical- October 1, 2018. sciences/edinburgh-imaging), Division of Clinical Neuro- sciences, University of Edinburgh, a core area of the Wellcome References 1. Vermeer SE, Longstreth WT Jr, Koudstaal PJ. Silent brain infarcts: a systematic Trust Clinical Research Facility, and part of the SINAPSE review. Lancet Neurol 2007;6:611–619. (Scottish Imaging Network: A Platform for Scientific Excellence) 2. Longstreth WT Jr. Brain vascular disease: overt and covert. Stroke 2005;36: 2062–2063. collaboration (sinapse.ac.uk), funded by the Scottish Funding 3. Saini M, Ikram K, Hilal S, Qiu A, Venketasubramanian N, Chen C. Silent stroke: not Council and the Chief Scientist Office. Collection of the Munich listened to rather than silent. Stroke 2012;43:3102–3104. 4. Debette S, Beiser A, DeCarli C, et al. Association of MRI markers of vascular brain cases and data analysis was supported by the Vascular Dementia injury with incident stroke, mild cognitive impairment, dementia, and mortality: the Research Foundation. This project has received funding from the Framingham Offspring Study. Stroke 2010;41:600–606. ’ 5. Bernick C, Kuller L, Dulberg C, et al. Silent MRI infarcts and the risk of future stroke: European Union s Horizon 2020 research and innovation pro- the Cardiovascular Health Study. Neurology 2001;57:1222–1229. gramme under grant agreements no. 666881, SVDs@target (to 6. Longstreth WT Jr, Dulberg C, Manolio TA, et al. Incidence, manifestations, and M.D.) and no. 667375, CoSTREAM (to M.D.); the DFG as part predictors of brain infarcts defined by serial cranial magnetic resonance imaging in the elderly: the Cardiovascular Health Study. Stroke 2002;33:2376–2382. of the Munich Cluster for Systems Neurology (EXC 1010 7. Schilling S, DeStefano AL, Sachdev PS, et al. APOE genotype and MRI markers of SyNergy) and the CRC 1123 (B3) (to M.D.); the Corona cerebrovascular disease: systematic review and meta-analysis. Neurology 2013;81: 292–300. Foundation (to M.D.); the Fondation Leducq (Transatlantic 8. Morrison AC, Ballantyne CM, Bray M, Chambless LE, Sharrett AR, Boerwinkle E. Network of Excellence on the Pathogenesis of Small Vessel LPL polymorphism predicts stroke risk in men. Genet Epidemiol 2002;22:233–242. 9. van Rijn MJ, Bos MJ, Yazdanpanah M, et al. Alpha-adducin polymorphism, athero- Disease of the Brain) (to M.D.); the e:Med program (e:Athe- sclerosis, and cardiovascular and cerebrovascular risk. Stroke 2006;37:2930–2934. roSysMed) (to M.D.) and the FP7/2007-2103 European Union 10. Song J, Kim OJ, Kim HS, et al. Endothelial nitric oxide synthase gene polymorphisms and the risk of silent brain infarction. Int J Mol Med 2010;25:819–823. project CVgenes@target (grant agreement no. Health-F2-2013- 11. Jenny NS, Tracy RP, Ogg MS, et al. In the elderly, interleukin-6 plasma levels and the 601456) (to M.D.). M.F. and A.H. acknowledge support from -174G>C polymorphism are associated with the development of cardiovascular dis- ease. Arterioscler Thromb Vasc Biol 2002;22:2066–2071. the BHF Centre of Research Excellence in Oxford and the 12. Fornage M, Chiang YA, O’Meara ES, et al. Biomarkers of inflammation and MRI- Wellcome Trust core award (090532/Z/09/Z). VISP: The defined small vessel disease of the brain: the Cardiovascular Health Study. Stroke 2008;39:1952–1959. GWAS component of the Vitamin Intervention for Stroke Pre- 13. Serizawa M, Nabika T, Ochiai Y, et al. Association between PRKCH gene poly- vention (VISP) study was supported by the US National Human morphisms and subcortical silent brain infarction. Atherosclerosis 2008;199:340–345. Genome Research Institute (NHGRI), grant U01 HG005160 14. van Oijen M, Cheung EY, Geluk CE, et al. Haplotypes of the fibrinogen gene and cerebral small vessel disease: the Rotterdam scan study. J Neurol Neurosurg Psy- (PI Mich`ele Sale and Bradford Worrall), as part of the Genomics chiatry 2008;79:799–803. and Randomized Trials Network (GARNET). Genotyping 15. Jeon YJ, Kim OJ, Kim SY, et al. Association of the miR-146a, miR-149, miR-196a2, and miR-499 polymorphisms with ischemic stroke and silent brain infarction risk. services were provided by the Johns Hopkins University Center Arterioscler Thromb Vasc Biol 2013;33:420–430. for Inherited Disease Research (CIDR), which is fully funded 16. Debette S, Bis JC, Fornage M, et al. Genome-wide association studies of MRI-defined brain infarcts: meta-analysis from the CHARGE Consortium. Stroke 2010;41:210–217. through a federal contract from the NIH to Johns Hopkins 17. Zhu YC, Dufouil C, Tzourio C, Chabriat H. Silent brain infarcts: a review of MRI University. Assistance with data cleaning was provided by the diagnostic criteria. Stroke 2011;42:1140–1145. 18. Verhaaren BF, Debette S, Bis JC, et al. Multiethnic genome-wide association study of GARNET Coordinating Center (U01 HG005157; PI Bruce S. cerebral white matter hyperintensities on MRI. Circ Cardiovasc Genet 2015;8: Weir). Study recruitment and collection of datasets for the VISP 398–409. 19. Genomes Project C, Abecasis GR, Auton A, et al. An integrated map of genetic clinical trial were supported by an investigator-initiated research variation from 1,092 human genomes. Nature 2012;491:56–65. grant (R01 NS34447; PI James Toole) from the US Public 20. Morris AP. Transethnic meta-analysis of genomewide association studies. Genet Epidemiol 2011;35:809–822. Health Service, NINDS, Bethesda, MD. Control data obtained 21. Willer CJ, Li Y, Abecasis GR. METAL: fast and efficient meta-analysis of genomewide through the database of genotypes and phenotypes (dbGAP) association scans. Bioinformatics 2010;26:2190–2191. e502 Neurology | Volume 92, Number 5 | January 29, 2019 Neurology.org/N 22. Wang X, Chua HX, Chen P, et al. Comparing methods for performing trans-ethnic meta- 34. Volpe M, Rosei EA, Ambrosioni E, Leonetti G, Trimarco B, Mancia G. Reduction in analysis of genome-wide association studies. Hum Mol Genet 2013;22:2303–2311. estimated stroke risk associated with practice-based stroke-risk assessment and 23. Han B, Eskin E. Random-effects model aimed at discovering associations in meta- awareness in a large, representative population of hypertensive patients: results from analysis of genome-wide association studies. Am J Hum Genet 2011;88:586–598. the ForLife study in Italy. J Hypertens 2007;25:2390–2397. 24. Franceschini N, van Rooij FJ, Prins BP, et al. Discovery and fine mapping of serum 35. Hankey GJ, Study I, the ET. Stroke: fresh insights into causes, prevention, and protein loci through transethnic meta-analysis. Am J Hum Genet 2012;91:744–753. treatment. Lancet Neurol 2011;10:2–3. 25. Beecham GW, Hamilton K, Naj AC, et al. Genome-wide association meta-analysis of 36. Tikhonoff V, Zhang H, Richart T, Staessen JA. Blood pressure as a prognostic factor neuropathologic features of Alzheimer’s disease and related dementias. PLoS Genet after acute stroke. Lancet Neurol 2009;8:938–948. 2014;10:e1004606. 37. Fanning JP, Wong AA, Fraser JF. The epidemiology of silent brain infarction: a sys- 26. Evans DM, Davey Smith G. Mendelian randomization: new applications in the coming tematic review of population-based cohorts. BMC Med 2014;12:119. age of hypothesis-free causality. Annu Rev Genomics Hum Genet 2015;16:327–350. 38. Freiberg JJ, Tybjaerg-Hansen A, Jensen JS, Nordestgaard BG. Nonfasting triglycerides 27. Bowden J, Davey Smith G, Burgess S. Mendelian randomization with invalid and risk of ischemic stroke in the general population. JAMA 2008;300:2142–2152. instruments: effect estimation and bias detection through Egger regression. Int J 39. Emerging Risk Factors C, Di Angelantonio E, Sarwar N, et al. Major lipids, apoli- Epidemiol 2015;44:512–525. poproteins, and risk of vascular disease. JAMA 2009;302:1993–2000. 28. Zhong H, Prentice RL. Bias-reduced estimators and confidence intervals for odds 40. Schilling S, Tzourio C, Dufouil C, et al. Plasma lipids and cerebral small vessel disease. ratios in genome-wide association studies. Biostatistics 2008;9:621–634. Neurology 2014;83:1844–1852. 29. Trask TM, Ritty TM, Broekelmann T, Tisdale C, Mecham RP. N-terminal domains of 41. Bulik-Sullivan B, Finucane HK, Anttila V, et al. An atlas of genetic correlations across fibrillin 1 and fibrillin 2 direct the formation of homodimers: a possible first step in human diseases and traits. Nat Genet 2015;47:1236–1241. microfibril assembly. Biochem J 1999;340:693–701. 42. Carmelli D, DeCarli C, Swan GE, et al. Evidence for genetic variance in white matter 30. Ratnapriya R, Zhan X, Fariss RN, et al. Rare and common variants in extracellular hyperintensity volume in normal elderly male twins. Stroke 1998;29:1177–1181. matrix gene Fibrillin 2 (FBN2) are associated with macular degeneration. Hum Mol 43. Atwood LD, Wolf PA, Heard-Costa NL, et al. Genetic variation in white matter Genet 2014;23:5827–5837. hyperintensity volume in the Framingham Study. Stroke 2004;35:1609–1613. 31. Warren HR, Evangelou E, Cabrera CP, et al. Genome-wide association analysis 44. Fornage M, Debette S, Bis JC, et al. Genome-wide association studies of cerebral white identifies novel blood pressure loci and offers biological insights into cardiovascular matter lesion burden: the CHARGE consortium. Ann Neurol 2011;69:928–939. risk. Nat Genet 2017;49:403–415. 45. Arvanitakis Z, Capuano AW, Leurgans SE, Buchman AS, Bennett DA, Schneider JA. 32. Del-Aguila JL, Beitelshees AL, Cooper-Dehoff RM, et al. Genome-wide association The relationship of cerebral vessel pathology to brain microinfarcts. Brain Pathol analyses suggest NELL1 influences adverse metabolic response to HCTZ in African 2017;27:77–85. Americans. Pharmacogenomics J 2014;14:35–40. 46. Vermeer SE, Prins ND, den Heijer T, Hofman A, Koudstaal PJ, Breteler MM. Silent 33. Fanning JP, Wesley AJ, Wong AA, Fraser JF. Emerging spectra of silent brain in- brain infarcts and the risk of dementia and cognitive decline. N Engl J Med 2003;348: farction. Stroke 2014;45:3461–3471. 1215–1222.

Neurology.org/N Neurology | Volume 92, Number 5 | January 29, 2019 e503 Genetic and lifestyle risk factors for MRI-defined brain infarcts in a population-based setting Ganesh Chauhan, Hieab H.H. Adams, Claudia L. Satizabal, et al. Neurology 2019;92;e486-e503 Published Online before print January 16, 2019 DOI 10.1212/WNL.0000000000006851

This information is current as of January 16, 2019

Updated Information & including high resolution figures, can be found at: Services http://n.neurology.org/content/92/5/e486.full

References This article cites 46 articles, 19 of which you can access for free at: http://n.neurology.org/content/92/5/e486.full#ref-list-1 Permissions & Licensing Information about reproducing this article in parts (figures,tables) or in its entirety can be found online at: http://www.neurology.org/about/about_the_journal#permissions Reprints Information about ordering reprints can be found online: http://n.neurology.org/subscribers/advertise

Neurology ® is the official journal of the American Academy of Neurology. Published continuously since 1951, it is now a weekly with 48 issues per year. Copyright Copyright © 2019 The Author(s). Published by Wolters Kluwer Health, Inc. on behalf of the American Academy of Neurology.. All rights reserved. Print ISSN: 0028-3878. Online ISSN: 1526-632X.